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Explore Semantic Web concepts, linking data meaningfully for smarter applications and improved search.

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Course Detail

The Semantic Web course explores data provenance, RDF, SPARQL, and ontologies for building intelligent web applications.

Course Features

  • Semantic Web basics
  • Linked data principles
  • Data lineage concepts
  • Knowledge representation methods

Lexsense

Instructor in Marketing

Chakir Mahjoubi is a language technology researcher focusing on Semantic Web, NLP, and knowledge representation through RDF, SPARQL, and ontology-based systems.

  • 20 hours on-demand video
  • Full Lifetime Access
  • Access on Mobile and TV
  • Certificate of Completion

Course Content

11 sections • 28 lecture • 19h 33m total length

The Semantic Web extends the traditional web by enabling machines to understand and interpret data meaningfully. It introduces standards for linking and structuring information across systems. This section covers its evolution, principles, and real-world applications.

Knowledge representation focuses on how information can be formally structured for machine understanding. It includes logic-based models, semantic networks, and graph structures. This foundation is essential for building intelligent systems.

RDF is a standard model for representing data as triples: subject, predicate, and object. It enables structured and interconnected data across the web. This section also covers RDF formats like XML, Turtle, and JSON-LD.

SPARQL is used to query and manipulate RDF datasets efficiently. It allows retrieval of structured data using flexible query patterns. Learners explore basic and advanced query operations on semantic data.

Ontology engineering involves designing formal models of domain knowledge. It defines classes, relationships, and constraints using OWL. This enables reasoning and semantic consistency in systems.

Linked Data connects datasets using shared identifiers and semantic links. Knowledge graphs organize information into meaningful, interconnected structures. This section explains how to build and publish such systems.

SPARQL is used to query and manipulate RDF datasets efficiently. It allows retrieval of structured data using flexible query patterns. Learners explore basic and advanced query operations on semantic data. 

Ontology engineering involves designing formal models of domain knowledge. It defines classes, relationships, and constraints using OWL. This enables reasoning and semantic consistency in systems. 

Linked Data connects datasets using shared identifiers and semantic links. Knowledge graphs organize information into meaningful, interconnected structures. This section explains how to build and publish such systems.

NLP enables computers to process and understand human language. It includes tasks like tokenization, entity recognition, and text analysis. These techniques support intelligent language-based applications.

This area combines language processing with structured semantic models. NLP techniques are used to extract and enrich RDF data. It enables automatic generation of knowledge graphs from text.

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  • I’ve learned so much from this platform—clear explanations, real‑world exercises, and expert instructors make mastering NLP, data, and linguistics both fun and rewarding.
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    Designer
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